Gesture control for monitoring vital body signs

ABSTRACT

The present invention reuses an accelerometer, or, more precise, sensed accelerations of a body sensor for user control of the body sensor. This is achieved by detecting predefined patterns in the acceleration signals that are unrelated to other movements of the patient. These include tapping on/with the sensor, shaking, and turning the sensor. New procedures are described that make it possible to re-use the acceleration sensing for reliable gesture detection without introducing many false positives due to non-gesture movements like respiration, heart beat, walking, etc.

FIELD OF THE INVENTION

The invention relates to an apparatus and method for controllingoperation of body sensors used for monitoring vital body signs.

BACKGROUND OF THE INVENTION

With the advances in embedded microcontrollers, inexpensive miniaturesensors, and wireless networking technologies, there has been a growinginterest in using wireless sensor networks in medical applications. Forexample, wireless sensor networks can replace expensive and cumbersomewired devices for pre-hospital and ambulatory emergency care whenreal-time and continuous monitoring of vital signs is needed. Moreover,body sensor networks can be formed by placing low-power wireless deviceson or around the body, enabling long-term monitoring of physiologicaldata.

Personal Emergency Response Systems (PERS) are provided, where a usercan use a button (PHB—Personal Help Button) to call for assistance.After the button has been pressed, a wireless telephone connection takescare that the help center of the PERS service operator can assist theuser. Recently, a fall detector, i.e. wireless sensor which may includean accelerometer, has been added to the PHB, so that calls for help canbe made without the need for an explicit button press.

Furthermore, for elderly patients and people with chronic diseases, anin-house wireless sensor network allows convenient collection of medicaldata while they are staying at home, thus reducing the burden ofhospital stay. The collected data can be passed onto the Internetthrough a PDA, a cell-phone, or a home computer. The care givers thushave remote access to the patient's health status, facilitatinglong-term rehabilitation and early detection of certain physicaldiseases. If there are abnormal changes in the patient status,caregivers can be notified in a timely manner, and immediate treatmentcan be provided.

Vitals signs like respiration rate and heart rate can be monitored by anew generation of sensors which use wireless connectivity and make useof novel sensing principles. An example of a novel sensing principle isthe use of inertial sensors (such as accelerometers, for example) tosense respiration rate, heart rate or other vital signs. In general,inertial measurement components sense either translational accelerationor angular rate. The advances in micro-electromechanical systems (MEMS)and other micro-fabrication techniques have greatly reduced the cost andthe size of these devices, and they can be easily embedded into wirelessand mobile platforms. Gyroscopes and accelerometers are two commoninertial sensors that can be used to capture human motion continuously.The wireless connectivity provides more comfort to the patient andsimplifies the operational usage. The sensor can be attached below theclothing of the patient, for patient convenience. However, this makes itcumbersome for the physician to operate the sensor: physically, to findthe sensor and knob, but socially, to reach below the clothes. What'smore, for hygienic reasons, the sensors are preferably completely sealedand free of knobs. This poses the problem of user control. Using thewireless connection may solve, but leaves the problem of initiating theconnection. Power consumption constraints prohibit the radio to beswitched on continuously to scan for potential commands.

The use of inertial sensors, such as accelerometers, for detection andclassification of human gestures introduces the problem of the reliabledistinction between user control commands (gestures) and other motions(movements by the patient as they occur in daily life). For example, inApplication Note AN2768: “LIS331 DL 3-axis digital MEMS accelerometer:translates finger taps into actions” by ST, June 2008, a tap detectionprocedure is described. The procedure is based on sensing theacceleration and identifying a tap when the signal surpasses a certainthreshold, while returning below the threshold within a prescribed timewindow. In a similar way, double taps are detected, by observing a pairof threshold crossings within a prescribed period where each crossing isof a prescribed duration. Although threshold crossing and timing areessential features for detecting a tap, they are not sufficient toobtain reliable detection, in the sense of a low rate of false positives(non-tapping movements that induce a similar signal that will pass thedetection procedure) acceptable for practical use. For example, uponheal strike during walking the acceleration signals can show peaks ofshort duration, and, hence, can trigger the detection of a “tap”.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a more reliabledistinction between user control commands (gestures) and other motionsin inertial based sensors and to enable simplified user control with noor less knobs, buttons or the like.

This object is achieved by an apparatus as claimed in claim 1, by a bodysensor device as claimed in claim 9, by a method as claimed in claim 10,and by a computer program product as claimed in claim 15.

Accordingly, sensed accelerations of a body sensor are used for usercontrol of the body sensor, which is realized by detecting predefinedgesture patterns in the acceleration output or signals, that areunrelated to the other movements by the patient. These may includetapping on/with the sensor, shaking, and turning the sensor. Ofparticular interest are those types of motions that can be performedwhile the sensor stays attached to the patient, possibly below theclothing. Now, instead of pushing the PHB button the user may also applya predetermined gesture, e.g., shake the device, in order to get a callconnection for help. As another option, the above mentioned PERS falldetector can be extended for vital sign monitoring as described above,or other quantities like stability of gait.

Another advantage of the proposed gesture control is “ease of use” orsimplicity. As an example, a nurse does not need to search in aninconvenient manner for the button on the device, particularly if thesensor device is below the pajama of a patient. Moreover, an elderlyperson in immediate need for help doesn't need to search for a buttonand just needs to shake the sensor device. Additionally, the sensordevice does not need a button any longer and can be cleaned more easily.According to a first aspect, the predetermined gesture may be a tapgesture, wherein the gesture detector is adapted to obtain at least oneone-dimensional signal component from the acceleration output, toestimate a background level and to detect a candidate tap if theone-dimensional signal component surpasses a first threshold and thebackground level is below a second threshold.

As an option of the first aspect, the gesture detector may be adapted topre-filter the acceleration output to obtain said one-dimensional signalcomponent, and to determine a tap detection event if the candidate tapappears in a predetermined sequence. The pre-filtering may be adapted toselect one component of the three-dimensional acceleration output, e.g.the one perpendicular to the patient's body. As another option, theacceleration output can be one-dimensional already (i.e. just aone-dimensional acceleration sensor is used in the sensor device).

Thus, a new algorithm is described that makes it possible to re-use theacceleration sensing for reliable tap detection without introducing manyfalse positives due to non-tap movements like respiration, heart beat,walking, or the like or accidental sensor movements, e.g. bumpingagainst an obstacle, and dropping the sensor.

In the above first aspect, the gesture detector may optionally beadapted to pre-filter the acceleration output by using a complementarymedian filter. Thereby, small peaks in the acceleration signal can bewell detected. Furthermore, according to another option, the gesturedetector may be adapted to estimate the background level by using anadaptive median filter. This ensures that false alarms are suppressed atthe edges of signals of longer duration. Moreover, according to anotheroption, the gesture detector may be adapted to detect the candidate tapby testing the maximum of the background level to be above a thirdthreshold. Thereby, accidental bumps do not lead to false alarms.

According to a second aspect which can be combined with the firstaspect, the predetermined gesture may be a turn gesture, wherein thegesture detector is adapted to analyze acceleration samples of theacceleration output on a frame by frame basis, to determine a referencevector within a frame, and to detect a turn gesture if an angle betweenthe reference vector and a series of acceleration samples is withinrange from a first threshold for at least a first predetermined numberof samples and thereafter below a second threshold for at least a secondpredetermined number of samples and thereafter within a third thresholdfor a third predetermined number of samples, which happens before atotal duration of a fourth predetermined number of samples. Thereby,turn gestures can be reliably detected and discriminated from othergestures.

According to a third aspect which can be combined with at least one ofthe first and second aspects, the predetermined gesture may be a shakegesture, wherein the gesture detector is adapted to observe each ofthree acceleration components of a three-dimensional acceleration outputof the inertial sensor, to compare the acceleration components withpredetermined positive and negative thresholds, and to determine a shakedetection event if for at least one of the acceleration components theacceleration crosses the positive threshold and the negative threshold aminimum number of times in alternating order and within a maximumduration. Thereby, shake gestures can be reliably detected anddiscriminated from other gestures.

In a further aspect of the present invention a computer program forperforming noise reduction is provided, wherein the computer programcomprises code means for causing the load monitoring apparatus to carryout the steps of the above method, when the computer program is run on acomputer controlling the load monitoring apparatus.

The above apparatus may be implemented as a hardware circuit, singlechip or chip set which can be mounted to a circuit board of a bodysensor. The chip or chip set may comprises a processor which iscontrolled by program or software routine.

It shall be understood that a preferred embodiment of the invention canalso be any combination of the dependent claims with the respectiveindependent claim. These and other aspects of the invention will beapparent from and elucidated with reference to the embodiments describedhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following drawings:

FIG. 1 shows a schematic block diagram of a body sensor in which thedetection procedures according to the embodiment can be implemented;

FIG. 2 shows a schematic flow diagram of a tap detection procedureaccording to a first embodiment;

FIG. 3 shows a schematic flow diagram of a turn detection procedureaccording to a second embodiment; and

FIG. 4 shows a schematic flow diagram of a shake detection procedureaccording to a third embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following embodiments, detection procedures for body sensors aredescribed that make it possible to re-use the acceleration sensing forreliable detection of gestures without introducing many false positivesdue to non-gesture movements. The approach is that in situations wherethe event to be detected is rare and where many comparable signals arehappening, the detection procedures and detectors are designed forspecificity first, i.e. an acceptable False Alarm rate (FA), andmaximizes sensitivity as much as possible, i.e. a maximal detectionprobability (PD).

Basically, the problem is that of confusion. An arbitrary movement mayinduce a similar signal as the gesture to be detected. The approach,therefore, is to design the detectors and detection procedures forvirtually no FA and to prescribe (constrain) the gesture movements thatwill be accepted. These movements are optimized for the intended usagescenarios and users. As a consequence, the user may need to issue thegesture again. This seems acceptable as long as the need to repeat thegesture is seldom and does not get annoying. Another consequence is theneed for some form of feedback that gesture is recognized, e.g. throughan light emitting diode (LED) shining through the housing or a smallspeaker that can emit beeps or similar sounds. Perhaps a littleexercising for first time users can be helpful, in which case moredetailed feedback on the (non)conformance with the prescribed gesturecan be effective.

In the following, detection of different specific gestures is describedin connection with three exemplary and non-restrictive embodiments. Eachdetection procedure is based on the typical characteristics thatdistinguish the gesture from any other movement or touch of the sensor.The typical characteristic of a tap is a peak of short duration. Thetypical characteristic of a turn is the direction of gravity moving toopposite direction and back again. The typical characteristic of a shakeis a set of alternating extreme accelerations.

FIG. 1 shows a schematic block diagram of a body sensor in which thedetection procedures according to the first to third embodiment can beimplemented. The body sensor comprises at least one acceleration sensor10 or other type of interial sensor for sensing accelerations caused bymovements of the body sensor and for outputting an analog or digitalthree-dimensional (3D) acceleration signal. The output signal of theacceleration sensor (ACC-S) 10 is supplied to a signal processingcircuit (SP) 20 which is adapted to detect or filter desired vital bodysigns to be monitored. The filtered vital body signs are supplied to aradio frequency (RF) front-end 30 in order to be wirelessly transmittedto a remote receiver unit (not shown) via an antenna unit. Of course,the filtered vital body signs could as well be transmitted to the remotereceiver unit via a wired transmission.

Furthermore, according to the embodiments, a gesture detector ordetection unit (GD) 40 is provided, which receives the output signal ofthe acceleration sensor 10 (or a filtered version thereof) and processesthe received signal so as to detect at least one predetermined gesturewhich can be used to control the operation of the body sensor. Toachieve this, the gesture detection unit 40, which may be a signalprocessor controlled by a program or software routine to implement adesired detection procedure or algorithm, provides a control input to asensor control circuit or processor (DC) 50. As an example, thedetection of a predetermined gesture, e.g. double tap, may switch on theradio to search for a base station for further communication (datatransmission) or control (by means of a user interface (UI) on the basestation). The signal processor 20, the gesture detection unit 40 and thesensor control processor 50 may be implemented by a single processor orcomputer device based on corresponding programs or software routines. Inprinciple, the computation can also be performed outside the device,i.e. the (wireless) connection transmits the raw/partly processed sensordata.

In the following first embodiment, the gesture detection unit 40 of thebody sensor of FIG. 1 is provided with a tap detection functionality.The typical characteristic of a tap is a short isolated spike in theacceleration signal. The tapping gesture is defined as double tapping bya finger or the hand against the body sensor. The body sensor is assumedto be attached to a human body, typically the waist. The human body isassumed to be at rest, i.e. not in a motion.

The gesture detector 40 may be adapted to filter accelerometer data fromthe acceleration sensor 10 to create a signal that emphasizes shortpeaks. Gestures like tapping are not the only movements that causesshort peaks. For example, heel strikes during walking also cause suchpeaks. Therefore, second characteristics may be that the gesture happensin the absence of other activity, and that the desired gesture is madeup of a predetermined sequence of events (e.g. double tap). Thesecharacteristics restrict the freedom of use, but considerably improvethe rejection of false alarms.

FIG. 2 shows a schematic flow diagram of the tap detection procedureaccording to the first embodiment, which comprises the processing stepsor blocks of pre-filtering, background level estimation, tap detectionand tap selection. The tap detection procedure comprises a pre-filteringstep S101, where three-dimensional (3D) acceleration signals of theacceleration sensor(s) 10 are processed into a one-dimensional (1D)signal such that short duration peaks get enhanced. Then, in thesubsequent step S102 a background level is estimated from the 1D signal.In a subsequent tap detection step S103, candidate taps are detected ifthe 1D signal surpasses a threshold, provided that the background levelis below another threshold. Then, in the final tap selection step S104,from the remaining taps those that appear in pairs are selected andidentified as a tap detection event.

Where traditional tap detection procedures focus on the peak exceeding athreshold, i.e. the sensitivity, the proposed tap detection procedureaccording to the first embodiment provides specificity by first testingon a low background level. A double tap is required, since single taplike events can still happen in the absence of other activity. Anarbitrary design choice is to accept or to reject triple taps. In theexemplary first embodiment, rejection of triple taps has been chosen.

The pre-filtering step S101 makes use of non-linear filters, so as toenhance the short, isolated, spiky character of the taps. A medianfilter is known to suppress spikes, in other words providing an estimateof the background. The peak itself will not raise the estimate. Viceversa, by applying the median filter in a complementary manner thespikes are found, suppressing the background.

Moreover, depending on the window size the filter is using, thebackground that will result from a tapping event that is not embedded infurther activity can disappear in such a filter, while the background ofa movement of longer duration will stay. In this way the backgroundlevel will initially stay low, but will jump to larger values when themovement takes longer. The estimate in background level obtained in stepS102 will rise more than proportionally with duration of the activity,in this way improving specificity. Only spiky movements of shortduration can pass the detector, which movements in general are the(double) tap.

More specifically, the pre-filtering in step S101 may consist of aso-called complementary median filter, which is a traditional medianfilter as described for example in J. Astola and P. Kuosmanen,“Fundamentals of nonlinear digital filtering”, CRC Press, 1997, howeverreturning the complement of the filtered signal. The complement is theoriginal value from which the (traditionally) filtered value issubtracted. As an example, a half-window length of 0.5 s may be used.The filter is applied to each of the three components of the 3Dacceleration signal. Then, after the filter, the L1 norm of the filteredsignals is taken. It was found that this order (first filter, then L1norm) yielded the most boost of small peaks in the acceleration signal.Also, the L1-norm was found to be more sensitive than the L2-norm, i.e.the L1-norm enhances spikes better than the L2-norm. The L1-norm, alsoknown as the Manhattan distance, is the sum of the absolute values ofthe vector's components. The L2-norm, also known as the Euclideandistance, is the square root of the sum of squared values of thevector's components.

If implementation costs or other reasons do not leave room, it isexpected that the order can be reversed (first norm, then complementarymedian). In particular at low sampling rates and tapping on a hardsurface (e.g., a table), the described order is more sensitive. Anothercost saving could be in using only one component (dimension) of the(differentiated) acceleration signal, for example only using the oneperpendicular to the user's body.

A way to enhance the spikiness of the signal is to apply the filter onthe derivatives of the acceleration signal. The first derivative isknown as “jerk”, the second as “snap” (“crackle” and “pop” for third andfourth derivates).

However, concerning the estimation of the background level in step S102,described next, experiments revealed differentiation will reducespecificity. The aim is that upon a double tap a low estimate of thebackground level results, while during any other movement a largeestimate results. The ratio between the estimated background level whenusing differentiation and the background level when using the(undifferentiated) acceleration data indicates that differentiation hasthe opposite effect to what the aim is. Upon a double tap the ratio islarge, while during the walking movement it is low. Hence, as far asestimation of the background level is concerned the (undifferentiated)accelerometer data should be used in step S102.

The principle of the background level step S102 is to suppress tapdetection in case of background activity. As already said, thebackground level is estimated using a second nonlinear filter. It isbasically a median filter, so that the spikes themselves get removed. Inthis way, a fair estimate of the signal background is obtained thatquickly follows increments and decrements, while spikes, in particulartaps, do not contribute to the estimated level. If the background levelestimated in this way surpasses a threshold, the further tap detectionis disabled. As an example, a threshold value of 1.2 m/s² can be used.

A traditional median filter provides an estimate of the background levelof a spiky signal. Due to its nonlinear character a low backgroundestimate results in case the signal is of short duration. This is abeneficial effect, since such short signals, if spiky in addition, aremost likely due to tapping the sensor. However, at the edges of a signalof longer duration the background estimate will neither rise immediatelyto the higher level, since the window is largely covering the non-activesignal part. This may postpone the suppression of the further tapdetection procedure and hence may lead to false alarms.

This problem can be solved by using an adaptive median filter. In such afilter the window length can be adaptively chosen, as described forexample in H. Hwang and R. A. Haddad, “Adaptive median filters—newprocedures and results”, IEEE Trans. Image Proc. 4 (4), 499-502, 1995.Basically, the window size can be adapted depending on the rank order ofthe median from subsequent subwindows. This is similar to the so-calledpermutation filter described for example in J. Astola and P. Kuosmanen,“Fundamentals of nonlinear digital filtering”, CRC Press, 1997, whichalso selects an outcome based on the rank order over time. Thedifference is that the permutation filter selects from subwindows offixed size, where in the present first embodiment the window size isadapted.

The filter operates as follows. First, the window around the currentsample in the signal is split into three subwindows, and the median ineach of these subwindows is computed. Then, based on the rank pattern ofthe three subsequent medians, the following rule base is applied:

If the median computed over the center subwindow is the maximum of thethree medians, compute the median over a subwindow of double size tothat of the center subwindow.

If the median computed over the center subwindow is the middle betweenthe other two, compute the median over the center subwindow and thesubwindow holding the maximum median.

If the median computed over the center subwindow is the minimum of thethree medians, use the median of the center subwindow.

As an example, a half window length of 0.2 s can be used for thesubwindows. The window length is adapted to improve rising together withan onset, while a low estimate stays in case of an (isolated) tap. Incase of an isolated tap the center subwindow will return the largestmedian, so that the double window length, induced by the rule base, willcause a lower median value, hence further reducing the estimate of thebackground level. At the onset of a longer activity the three medianvalues will be ranked in the direction of the onset, and the median willbe taken over a stronger signal segment, hence yielding a largerestimate of the background level. It is however noted that the aboverule base merely provides an example how the window size can be adapted.For example, the doubling in the first rule could, of course, also beany other form of enlarging the window size.

A refinement is to perform some form of averaging over the computedbackground levels. For example, a power level p could be computed fromthe obtained background level values b as follows:

p=(1/NΣb ² [k])^(1/2),

where N is the length of the averaging window.

In case of averaging a simpler estimation of the background level couldbe used, e.g. a traditional median filter. In the first embodiment, theadaptive median filter can be used without further computation of apower level.

The background power estimation can also be used to control powerconsumption by the sensor. At high power levels the backgroundactivities behave like noise to the sensing measurements and accurateestimations are more difficult. Battery power can be saved by disablingthese measurements (until background is low enough).

In the tap detection step S103, those parts of the signal for which thebackground level was below the associated threshold are tested for peaksexceeding a second threshold. As an example, a threshold value of 7.2m/s² can be used.

A peak is the sample with maximum value over a continuous range ofsamples that are above this second threshold. The range is not strictlycontinuous in that short drops below the second threshold are permitted.As an example, a maximum drop duration of 0.09 s can be used. For beinga tap, the range should be of short duration. This is not tested,however, since in that case the background level will surpass itsthreshold.

Before accepting the found peak as a tap, an optional third thresholdtest is performed. In this test the maximum of the background level overthe found range is tested to be above a third threshold. As an example,a threshold value of 0.1 m/s² can be used here. If that maximum inbackground level is below the third threshold, the tap is rejected. Thistest is added to enhance specificity (decrease false alarm rate). It wasfound that when the sensor is lying on the desk (or other solid body,e.g. its charging unit) accidental bumps may cause (double) tap events.Such accidental bumps can happen by slightly lifting the sensor andletting it fall back on the desk (which perhaps happens when taking thesensor out of its charging unit but loosing it to slip back). In thosesituations the corresponding background level is quite low and less thanthe situations in which the sensor is held quietly in the hand orattached against the silent human body. The trade-off is a loss ofsensitivity for tapping the sensor when it is lying on the desk (orother solid body). It depends on the use scenario whether this is anacceptable trade-off or not.

In the final tap selection step S104, the found taps are tested whetherthey appear in groups. In the first embodiment only pairs of taps areaccepted and cause a detection event. All other group sizes arerejected. A “tap period” is defined as the duration between the two tapsof a double tapping event. In informal tests with 16 users showed thatthe typical distance between the peaks from a double tap is 9 to 17samples at 50 Hz sampling rate, i.e. 0.18-0.34 s. Thus, as an example,0.3 s can be used as tap period. Optionally, this value could be madeconfigurable or adaptive.

A tap is considered to belong to a group if it is within a certainduration from the previous tap. As an example, a duration of 1.3 timesthe above tap period can be used. Before testing whether a tap forms agroup with the previous tap, another test is performed, which is calledproximity rejection. In this test, if two taps appear too close to eachother, one is rejected. This test further improves the specificity,since it is unlikely that a person is tapping that fast. As an example,a duration of 0.3 times the above tap period can be used as boundaryduration. In case two taps are considered too close, the tap of smallestmagnitude could be rejected. A refinement of this rule could be toconsider the distance with the next tap as well. Proximity rejectionrelates with the drop durations that are permitted in the “continuous”range in the tap detection phase. They cannot be combined in a singletest, however.

A tap is detected if a tap group of two taps is found. In the exemplaryembodiment, single taps and groups of more than two taps are discardedand do not fire a tap detection. Of course, embodiments with multiplegesture detection units 40 for detecting different gestures (tap groupsor other types of gestures) can be provided as well. In animplementation, they can be integrated for optimal load on computationalresources and battery power consumption.

In the following second embodiment, the gesture detection unit 40 of thebody sensor of FIG. 1 is provided with a turn detection functionality.The turning gesture is defined as holding the sensor in the hand,holding the hand quietly for a short period, quickly fully turning thehand to reversed orientation (“180 degrees”), optionally pause veryshortly, quickly turning back, and hold quiet again for a short period.The sensor can be at arbitrary orientation in the hand. The hand isturned around a virtual axis in the (close to) horizontal plane.Typically, the turn is made by turning the wrist or by turning the arm(so that sensor moves upside down and back again). The full turnsuggests a 180 degrees rotation of the sensor. Physically, however, itis more of a 90 degrees rotation that happens.

FIG. 3 shows a schematic flow diagram of the turn detection procedureaccording to the second embodiment. In step S201 the 3D accelerometersignal is analyzed on a frame per frame basis. In the prototype a framesize of 1.8 s is used. The number of samples to shift to the next frameis dependent on whether a turn, a partial turn, or no turn is detectedin the current frame. A partial turn can complete in the next frame.Absence of a turn can in fact include the first holding period.Therefore, when shifting to the next frame at least the number ofsamples from the potential turn should stay in that next frame.

Then, in step S202 the procedure determines a reference vector. This canbe a fixed, predetermined vector, e.g. corresponding to the orientationof the sensor when it is in its usual position (e.g. “upside up”). Asanother example, this could also be the major acceleration vector withina frame, which is subsequently normalized to unit size. The majoracceleration vector is that vector (acceleration sample) to which allothers are closest. In other words, the major acceleration vector is themode in the distribution of acceleration samples. Such a mode can beestimated through a gamma filter, as described for example in J. Astolaand P. Kuosmanen, “Fundamentals of nonlinear digital filtering”, CRCPress, 1997, where γ→0. For each sample k in the frame the filtercomputes the product of the distances of all other samples j to thecurrent sample:

Π_(j≠k)|acc[j]−acc[k]|.

The sample k for which this product is minimal is selected as the majoracceleration vector. Instead of the conventional L2-norm, the L1-norm isused in the second embodiment to compute the distance |acc[j]−acc[k]|.

After obtaining the reference vector the dot product z between thisvector and each of the other acceleration samples is computed in stepS203.

Finally, in step S204 a decision about a turning gesture is made basedon predetermined up and down thresholds thresUp and thresDn. A turn isdetected in step S204 if the sequence z meets the following pattern:

z>thresUp, for at least upSz0 samples, whereafter

z<thresDn, for at least dnSz samples, whereafter

z>thresUp, for at least upSz1 samples, which happens before a totalduration of turnSz samples.

In the prototype we use the following values (the reference vector is ofunit size): upSz0=0.36 s, dnSz=0.24 s, upSz1=0.36 s, turnSz=1.8 s,thresUp=8.4 m/s² (inclination more than 60 degrees “upwards”, i.e. inthe direction of the major acceleration vector), thresDn=−5.6 m/s²(inclination more than 30 degrees “downwards”). As an example, theframes may be of the same size as turnSz. A frame is the size of theprocessing window over which the major acceleration vector isdetermined.

Experiments indicated the total processing is the fastest, i.e. thecomputational load is the least, if the frame size is equal to turnSz.

The above exemplary procedure can be generalized as follows. An anglebetween the reference vector and a series of samples is within a firstthreshold for a first predetermined number of acceleration samples andthereafter beyond a second threshold for at least a second predeterminednumber of acceleration samples and thereafter within a third thresholdfor a third predetermined number of acceleration samples, all within afourth predetermined number of acceleration samples. Here, “threshold”is to be understood as a range. For example, the angle in the firstseries is close to zero degrees, i.e. within, for example arange/threshold of ±5 degrees. In the second series the angle is beyond,for example, 90 degrees, i.e. a range of 90 to 180 degrees. In the thirdseries the angle is, for example, again in the range of −5 to 5 degrees.

Two gestures different from turning are known to cause a turn detection,i.e. a FA. They are a full rotation of the sensor, e.g. when tumblingthe sensor around in the hand, and shaking the sensor. Therefore, theprocedure is optionally extended with additional tests to prevent theseFA.

In order to suppress full rotations, it is in addition required that theacceleration vector is in the same half space upon its two crossings(first downwards, then upwards) through the plane perpendicular to themajor acceleration vector. This is the case for a back & forth turn, butnot for a full rotation around. The test is implemented by computing thedot product of the two acceleration vectors at the moment z changessign. It is positive (same half space) for a turn, while negative(opposite half spaces) for a full rotation. The test is only performedin case of a “gentle turn” where |acc|, the L2-norm of the sensedacceleration, is close to 1 g, not affected by turning the sensor alonga free fall trajectory (causing weak acceleration vectors, and henceunpredictable signs), and neither affected by a fiercefull rotation(causing the acceleration due to gravity to be flooded by centrifugalacceleration, and hence enforcing identical sign for both turn androtation). A free fall trajectory can happen when turning the sensorthrough turning the arm. As an example, 4<|acc|<19 can be required for agentle turn.

It is difficult and therefore unlikely to make a natural motion thatfully rotates the sensor along a free fall trajectory within thedetection time limit. A fast full rotation is possible to performfiercefully, but seems to happen only intentionally—just like the turn.The detection time limit requirement, turnSz, poses a limitation on therange of accepted gestures, but is in line with the above explainedapproach to design the detectors and detection procedures for virtuallyno FA and to prescribe (constrain) the gesture movements that will beaccepted.

By applying a low-pass filter (LPF) on the original 3D accelerationsignal, the signal z becomes more “gentle”. Indeed, in this wayfiercefull rotations could be detected (and suppressed). However, thedownward values of z are also smoothed out by the LPF and z<thresDn maynot happen anymore, reducing the detection rate. A mixed solution is todetect turns using the unfiltered acceleration signal, while thefiltered version is used to identify rotations. Since an LPF increasesthe computational load, no LPF is applied in the present embodiment.Shaking is suppressed by requiring that during the down phase, z<thresDnmust hold contiguously for the dnSz samples. A stronger criterium is toalso require that both upwards spans, z>thresUp, are contiguous. Anotherapproach is to detect the (simultaneous) shake, implying the detectedturn is a FA. In the second embodiment, only the contiguous down phasemay be applied.

In experiments it was found that some users start the turn back movementimmediately after the turn forward, i.e. skipping the prescribed pause.Setting dnSz to 0.12 s made the procedure sensitive to those gestures aswell. However, it also caused some shakes to be detected as turn. Theabove measures can counter this reduction in specificity. As alreadymentioned dnSz=0.24 s may be used.

The computational load can be reduced by omitting the determination ofthe major acceleration and requiring that the sensor should be held withprescribed orientation. This will also reduce the FA rate, since allother orientations are excluded for detection.

In the following third embodiment, the gesture detection unit 40 of thebody sensor of FIG. 1 is provided with a shake detection functionality.The typical characteristic of a shake is a sequence of alternatingextreme accelerations. The shake detection procedure is a simple, yetrobust procedure.

FIG. 4 shows a schematic flow diagram of the shake detection procedureaccording to the third embodiment. In step S301 the procedure observeseach of the three components of the acceleration signal separately.Then, in step S302 the acceleration components are compared withpredetermined positive and negative thresholds. In a final shakedecision step S303, a shake is detected, if for at least one of theacceleration components the acceleration crosses the positive thresholdand the negative threshold a minimum number of times in alternatingorder and within a maximum duration. It was found that upon shaking thesensor such a pattern appeared in one or two of the components,depending on the direction in which the sensor is shaked.

As an example, the thresholds can be set to plus and minus 16 m/s², theminimum number of required crossings can be set to 6, and the maximumduration to 0.9 s. In counting the number of crossings the signal isfirst monitored for being between the two thresholds. Then, everycrossing of a threshold yields a count, provided the threshold beingcrossed is the alternate from the previous crossing.

In principle, the shake detection procedure of the third embodiment canbe adapted to issue its acceleration output in positive numbers only, sothat the zero value of the term m/s² corresponds to a number somewherein the middle of the output range. Thus, the “positive” and “negative”thresholds may refer to values when the sensor output is or would havebeen calibrated such that the numbers correspond to a physicalacceleration in m/s².

The present invention can be used in patient monitoring, in particularrelated to wireless respiration and pulse sensors. The present inventioncan be applied in other fields as well, in particular, in the contextsof PERS subscribers who wear a pendant or wrist PHB. Pressing the PHBguarantees attention by the call center. It is known that finding theknob at the PHB can be cumbersome to the subscriber in need of help, inparticular to frail elderly. Gesture control can replace the need forpressing the button. A high false call rate is, however, not allowed,and a sensitive detection mechanism like the one described, is needed.New generation PHBs (with fall detection) host an accelerometer andprocessing capacity.

Additionally, energy expenditure through the sensor (accelerometer) wornby the user may be estimated. The estimation uses some “accelerationcounts” that are mapped to consumed calories. Since the mapping isdifferent for different activity types an average or most likely mappingis used. As an improvement, the user can control the mapping throughgesture control. For example, when starting to bike, a double tap setsthe mapping correspondingly. The sensor can stay in the pocket,providing easy control. Again, high sensitivity to discriminate the usercommand from other movements is needed.

Other variations to the disclosed embodiments can be understood andeffected by those skilled in the art in practicing the claimedinvention, from a study of the drawings, the disclosure, and theappended claims. In particular, at least two of the above gesturedetection procedures of the first to third embodiments can be combinedin a single embodiment to provide various control functions triggered bydifferent gestures.

To summarize, the present invention reuses an accelerometer, or, moreprecise, sensed accelerations of a body sensor for user control of thebody sensor. This is achieved by detecting predefined patterns in theacceleration signals that are unrelated to other movements of thepatient. These include tapping on/with the sensor, shaking, and turningthe sensor. New procedures have been described that make it possible tore-use the acceleration sensing for reliable gesture detection withoutintroducing many false positives due to non-gesture movements likerespiration, heart beat, walking, bumping, dropping the device, etc.

In the claims, the word “comprising” does not exclude other elements orsteps, and the indefinite article “a” or “an” does not exclude aplurality.

A single unit or device may fulfill the functions of several itemsrecited in the claims. The mere fact that certain measures are recitedin mutually different dependent claims does not indicate that acombination of these measures cannot be used to advantage.

The above steps S101 to S104 of FIG. 2, S201 to S204 of FIG. 3 and S301to S303 of FIG. 4 can be performed by a single unit or by any othernumber of different units which not necessarily need to be hosted ormounted in the sensor device. The calculations, processing and/orcontrol of the gesture detection unit 40 of FIG. 1 can be implemented asprogram code means of a computer program and/or as dedicated hardware.

The computer program may be stored/distributed on a suitable medium,such as an optical storage medium or a solid-state medium, suppliedtogether with or as part of other hardware, but may also be distributedin other forms, such as via the Internet or other wired or wirelesstelecommunication systems.

Any reference signs in the claims should not be construed as limitingthe scope.

The present invention reuses an accelerometer, or, more precise, sensedaccelerations of a body sensor for user control of the body sensor. Thisis achieved by detecting predefined patterns in the acceleration signalsthat are unrelated to other movements of the patient. These includetapping on/with the sensor, shaking, and turning the sensor. Newprocedures are described that make it possible to re-use theacceleration sensing for reliable gesture detection without introducingmany false positives due to non-gesture movements like respiration,heart beat, walking, etc.

1. An apparatus for controlling a sensor device which uses a movementsensor (10), said apparatus comprising: a gesture detector (40) forevaluating an acceleration output of said movement sensor (10) to detectat least one predetermined gesture; and a device controller (50) forcontrolling a functional operation of said sensor device in response toa detection output of said gesture detector (40), wherein said gesturedetector (40) is adapted to perform at least one of observing each ofthree acceleration components of a three-dimensional acceleration outputof said movement sensor (10), comparing said acceleration componentswith predetermined positive and negative thresholds, and determining ashake detection event if for at least one of said accelerationcomponents the acceleration crosses said positive threshold and saidnegative threshold a minimum number of times in alternating order andwithin a maximum duration, obtaining at least one one-dimensional signalcomponent from said acceleration output, estimating a background leveland detecting a candidate tap if said one-dimensional signal componentsurpasses a first threshold and said background level is below a secondthreshold, and analyzing acceleration samples of said accelerationoutput on a frame by frame basis, determining a reference vector withina frame, and detecting a turn gesture if an angle between said referencevector and a series of acceleration samples is within a range from afirst threshold for at least a first predetermined number of samples andthereafter below a second threshold for at least a second predeterminednumber of samples and thereafter within a third threshold for a thirdpredetermined number of samples, which happens before a total durationof a fourth predetermined number of samples.
 2. (canceled)
 3. Theapparatus according to claim 1, wherein said gesture detector (40) isadapted to detect a tap gesture and to pre-filter said accelerationoutput to obtain said one-dimensional signal component, and to determinea tap detection event if said candidate tap appears in a predeterminedsequence.
 4. The apparatus according to claim 3, wherein said gesturedetector (40) is adapted to detect a tap gesture and to pre-filter saidacceleration output by using a complementary median filter.
 5. Theapparatus according to claim 1, wherein said gesture detector (40) isadapted to a tap gesture and to estimate said background level by usingan adaptive median filter.
 6. The apparatus according to claim 1,wherein said gesture detector (40) is adapted to detect a tap gestureand to detect said candidate tap by testing the maximum of saidbackground level to be above a third threshold.
 7. (canceled) 8.(canceled)
 9. A body sensor device comprising: an inertial sensor (10)for sensing acceleration of said body sensor device; and an apparatusaccording to claim
 1. 10. A method of controlling a sensor device whichuses a movement sensor (10), said method comprising: evaluating anacceleration output of said movement sensor (10) to detect at least onepredetermined gesture; and controlling a functional operation of saidsensor device in response to a detection of said at least onepredetermined gesture; wherein said evaluating comprises at least one ofobserving each of three acceleration components of a three-dimensionalacceleration output of said movement sensor (10), comparing saidacceleration components with predetermined positive and negativethresholds, and determining a shake detection event if for at least oneof said acceleration components the acceleration crosses said positivethreshold and said negative threshold a minimum number of times inalternating order and within a maximum duration, obtaining at least oneone-dimensional signal component from said acceleration output,estimating a background level and detecting a candidate tap if saidone-dimensional signal component surpasses a first threshold and saidbackground level is below a second threshold, and analyzing accelerationsamples of said acceleration output on a frame by frame basis,determining a reference vector within a frame, and detecting a turngesture if an angle between said reference vector and a series ofacceleration samples is within a range from a first threshold for atleast a first predetermined number of samples and thereafter below asecond threshold for at least a second predetermined number of samplesand thereafter within a third threshold for a third predetermined numberof samples, which happens before a total duration of a fourthpredetermined number of samples.
 11. (canceled)
 12. The method accordingto claim 10, wherein said evaluating comprises detecting a candidatetape and further comprises pre-filtering said acceleration output toobtain said one-dimensional signal component, and determining a tapdetection event if said candidate tap appears in a predeterminedsequence.
 13. (canceled)
 14. (canceled)
 15. A computer program productcomprising code means for producing the steps of method claim 10 whenrun on a computing device.